class_docstr: Multi-level fused semantic segmentation head.
  
  .. code-block:: none
  
      in_1 -> 1x1 conv ---
                          |
      in_2 -> 1x1 conv -- |
                         ||
      in_3 -> 1x1 conv - ||
                        |||                  /-> 1x1 conv (mask prediction)
      in_4 -> 1x1 conv -----> 3x3 convs (*4)
                          |                  \-> 1x1 conv (feature)
      in_5 -> 1x1 conv ---
code: def forward(self, feats):
      x = self.lateral_convs[self.fusion_level](
          feats[self.fusion_level]
      )
      fused_size = tuple(x.shape[-2:])
      for i, feat in enumerate(feats):
          if i != self.fusion_level:
              feat = F.interpolate(
                  feat,
                  size=fused_size,
                  mode="bilinear",
                  align_corners=True,
              )
              x += self.lateral_convs[i](feat)
  
      for i in range(self.num_convs):
          x = self.convs[i](x)
  
      mask_pred = self.conv_logits(x)
      x = self.conv_embedding(x)
      return mask_pred, x
  
file_path: insightface/detection/scrfd/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py
imports: import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, kaiming_init
  from mmcv.runner import auto_fp16, force_fp32
  from mmdet.models.builder import HEADS
label_desc: Use image node_id 3 for calling the FusedSemanticHead obj's underlying member method code with example usage: obj.forward(feats) and returns: mask_pred,  x